Comorbidity package
David Henderson
24 August 2018
Introduction
A quick play with the comorbidity package to see the difference in Charlson and Elixhauser scores when using full and truncated ICD10 codes.
Packages
library(comorbidity)
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## extractInitialise dataframes
The package lets you make dummy data really easily. I’m going to make 30,000 observations of 5000 indiviudals (differing numbers of disease) then create a copy dataset with ICD10 codes truncated to 3 characters.
#Full data
data_full_code <- data.frame(
id = sample(1:5000, size = 30000, replace = TRUE),
code = sample_diag(n = 30000, version = "ICD10_2011"),
stringsAsFactors = FALSE
)
data_full_code %<>%
arrange(id)
#Truncated data
data_truncated <-
data_full_code %>%
mutate(code = strtrim(code, 3))
data_full_codedata_truncatedCalculate Charlson Index
Now run the comorbidity() function to create Charlson scores for both datasets.
charlson_full <- comorbidity(data_full_code, id = "id", code = "code", score = "charlson_icd10")
charlson_trunc <- comorbidity(data_truncated, id = "id", code = "code", score = "charlson_icd10")
charlson_fullcharlson_truncAre they the same
identical(charlson_full, charlson_trunc)## [1] FALSEOf course not, how different?
Visualise
Scores for full data
charlson_full %>%
ggplot(aes(score)) +
geom_bar()
…and truncated data. Slight drops in higher scores and increases in lower/zero scores as expected but not a huge difference
charlson_trunc %>%
ggplot(aes(score)) +
geom_bar()
The comorbidity() function also calculates a weighted score which increases scores for certain diseases (e.g. diabetes with complications). I’d expect to see a bigger difference here…
charlson_full %>%
ggplot(aes(wscore)) +
geom_bar()
truncated data…
charlson_trunc %>%
ggplot(aes(wscore)) +
geom_bar()
This holds up surprisingly well…
Elixhauser
Repeat for elixhauser
elix_full <- comorbidity(data_full_code, id = "id", code = "code", score = "elixhauser_icd10")
elix_trunc <- comorbidity(data_truncated, id = "id", code = "code", score = "elixhauser_icd10")
elix_fullelix_truncVisualise
Full codes
elix_full %>%
ggplot(aes(score)) +
geom_bar()
Truncated
elix_trunc %>%
ggplot(aes(score)) +
geom_bar()
Again, the same pattern where there are more zero and 1 scores and fewer higher scores. Again this is a very mild change though.
Weighted score full data
elix_full %>%
ggplot(aes(wscore)) +
geom_bar()
truncated
elix_trunc %>%
ggplot(aes(wscore)) +
geom_bar()
Again holds up better than expected.
Of course - this is dummy data and difficult to know how well this would work in the real world!
session_info
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## ui RTerm
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## tz Europe/London
## date 2018-08-24## Packages -----------------------------------------------------------------## package * version date source
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## comorbidity * 0.1.1 2018-03-30 CRAN (R 3.5.1)
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